A theoretical single-parameter model for urbanisation to study infectious disease spread and interventions

Optimizing Spatial Allocation of COVID-19 Vaccine by Agent-Based Spatiotemporal Simulations

Optimizing allocation of vaccine, a highly scarce resource, is an urgent and critical issue during fighting against on-going COVID-19 epidemic. Prior studies suggested that vaccine should be prioritized by age and risk groups, but few of them have considered the spatial prioritization strategy. This study aims to examine the spatial heterogeneity of COVID-19 transmission in the city naturally, and optimize vaccine distribution strategies considering spatial prioritization. We proposed an integrated spatial model of agent-based model and SEIR (susceptible-exposed-infected-recovered). It simulated spatiotemporal process of COVID-19 transmission in a realistic urban context. Individual movements were represented by trajectories of 8,146 randomly sampled mobile phone users on December 28, 2016 in Guangzhou, China, 90% of whom aged 18-60. Simulations were conducted under seven scenarios. Scenarios 1 and 2 examined natural spreading process of COVID-19 and its final state of herd immunity. Scenarios 3-6 applied four vaccination strategies (random strategy, age strategy, space strategy, and space & age strategy), and identified the optimal vaccine strategy. Scenario 7 assessed the most appropriate vaccine coverage. The results demonstrates herd immunity is heterogeneously distributed in space, thus, vaccine intervention strategies should be spatialized. Among four strategies, space & age strategy is substantially most efficient, with 7.7% fewer in attack rate and 44 days longer than random strategy under 20% vaccine uptake. Space & age strategy requires 30%-40% vaccine coverage to control the epidemic, while the coverage for a random strategy is 60%-70% as a comparison. The application of our research would greatly improves the effectiveness of the vaccine usability.

Changes in Anxiety, Depression, and Stress in 1 Week and 1 Month Later After the Wuhan Shutdown Against the COVID-19 Epidemic

Objective:

The aim of this study was to investigate the changes in Chinese residents’ psychological state and its influencing factors after the Wuhan shutdown on January 23, 2020.

Methods:

Two surveys were conducted on February 1-5 and February 20-24, separately, using an online self-administrated questionnaire among 3145 and 3814 participants, respectively. Subjective indicators of daily-life changes include level of attention, risk of infection, impact of daily life, self-perceived health status, and mental health help-seeking. Individual scores on changes in anxiety, depression, and stress were generated by 6-item, 4-item, and 3-item questions. A multivariate regression model was fitted in each survey, separately and combined.

Results:

A total of 6959 residents participated in the study, with 32.78% male and 67.22% female, people living in Wuhan and other cities in Hubei Province accounted for 25.22% and 18.85%, respectively. One week after the Wuhan shutdown, their anxiety, depression, and stress had all increased. Compared with the first survey, the changes in the scores of anxiety, depression, and stress in the second survey were decreased (β = −1.220, −0.798, and −0.623, all P < 0.001). The level of attention, risk of infection, and self-perceived health status tended to be positively associated with the changes in the scores of anxiety, depression, and stress.

Conclusions:

The study showed that the lives and psychological conditions of residents had undergone negative changes after the Wuhan shutdown, but the measures taken during this period were effective. These results may provide guidance for public health policies in other countries and regions.

Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh

Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014–2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of approximate Bayesian computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements.